For a given data set, exploring their multi-view instances under a clustering framework is a practical way to boost the clustering performance. This is because that each view might reflect partial information for the existing data. Furthermore, due to the noise and other impact factors, exploring these instances from different views will enhance the mining of the real structure and feature information within the data set. In this paper, we propose a multiple kernel spectral clustering algorithm through the multi-view instances on the given data set. By combining the kernel matrix learning and the spectral clustering optimization into one process framework, the algorithm can determine the kernel weights and cluster the multi-view data simultaneously. We compare the proposed algorithm with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.

en_US

dc.publisher

IEEE

en_US

dc.relation.ispartof

2014 22nd International Conference on Pattern Recognition (ICPR)

en_US

dc.relation.ispartof

International Conference on Pattern Recognition

en_US

dc.relation.isbasedon

10.1109/ICPR.2014.648

en_US

dc.title

Multiple Kernel Learning Based Multi-view Spectral Clustering

en_US

dc.type

Conference Proceeding

utslib.description.version

Published

en_US

utslib.location

Piscataway, USA

en_US

utslib.location.activity

Stockholm, Sweden

en_US

utslib.for

080101 Adaptive Agents and Intelligent Robotics

en_US

utslib.for

080110 Simulation and Modelling

en_US

utslib.for

080109 Pattern Recognition and Data Mining

en_US

pubs.embargo.period

Not known

en_US

pubs.organisational-group

/University of Technology Sydney

pubs.organisational-group

/University of Technology Sydney/Faculty of Engineering and Information Technology

pubs.organisational-group

/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering

For a given data set, exploring their multi-view instances under a clustering framework is a practical way to boost the clustering performance. This is because that each view might reflect partial information for the existing data. Furthermore, due to the noise and other impact factors, exploring these instances from different views will enhance the mining of the real structure and feature information within the data set. In this paper, we propose a multiple kernel spectral clustering algorithm through the multi-view instances on the given data set. By combining the kernel matrix learning and the spectral clustering optimization into one process framework, the algorithm can determine the kernel weights and cluster the multi-view data simultaneously. We compare the proposed algorithm with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.

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